Evidence-based risk assessment and recommendations for physical activity clearance: an introduction<sup>1</sup>This paper is one of a selection of papers published in this Special Issue, entitled Evidence-based risk assessment and recommendations for physical activity clearance, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
The Physical Activity Readiness Questionnaire (PAR-Q) and the Physical Activity Readiness Medical Evaluation (PARmed-X) are internationally renowned and extensively used preparticipation screening tools. However, recent feedback from end-users has identified limitations to the existing PAR-Q and PARmed-X screening process. As such, a systematic evaluation of the PAR-Q and PARmed-X forms was conducted, adhering to the Appraisal of Guidelines for Research and Evaluation (AGREE) criteria. Recognized experts in physical activity (PA) and prominent health conditions worked with an expert consensus panel to increase the effectiveness of the PAR-Q and PARmed-X PA participation clearance process. The systematic review process established that the health benefits of PA participation far outweigh the risks in the vast majority of asymptomatic and symptomatic individuals. A new risk continuum and decision tree process was created to allow for the effective risk stratification of prominent health conditions, reducing greatly the barriers to PA participation for the majority of individuals. The new PA participation clearance process is available in new paper and online versions (PAR-Q+) and the PARmed-X was replaced with an online interactive computer programme (ePARmed-X+). It is anticipated that this new risk stratification and PA clearance process will reduce markedly the barriers for PA participation for both asymptomatic and symptomatic individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.180 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".